Bahareh Behboodi, Francois-Xavier Carton, Matthieu Chabanas, Sandrine de Ribaupierre, Ole Solheim, Bodil K. R. Munkvold, Hassan Rivaz, Yiming Xiao, Ingerid Reinertsen
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To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery.</p>\n </section>\n \n <section>\n \n <h3> Acquisition and Validation Methods</h3>\n \n <p>The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, <i>sulci</i>, <i>falx cerebri</i>, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations.</p>\n </section>\n \n <section>\n \n <h3> Data Format and Usage Notes</h3>\n \n <p>Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk.</p>\n </section>\n \n <section>\n \n <h3> Potential Applications</h3>\n \n <p>The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance in neurosurgery.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 9","pages":"6525-6532"},"PeriodicalIF":3.2000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17317","citationCount":"0","resultStr":"{\"title\":\"Open access segmentations of intraoperative brain tumor ultrasound images\",\"authors\":\"Bahareh Behboodi, Francois-Xavier Carton, Matthieu Chabanas, Sandrine de Ribaupierre, Ole Solheim, Bodil K. R. Munkvold, Hassan Rivaz, Yiming Xiao, Ingerid Reinertsen\",\"doi\":\"10.1002/mp.17317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Acquisition and Validation Methods</h3>\\n \\n <p>The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, <i>sulci</i>, <i>falx cerebri</i>, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Data Format and Usage Notes</h3>\\n \\n <p>Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Potential Applications</h3>\\n \\n <p>The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. 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Open access segmentations of intraoperative brain tumor ultrasound images
Purpose
Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery.
Acquisition and Validation Methods
The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, sulci, falx cerebri, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations.
Data Format and Usage Notes
Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk.
Potential Applications
The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance in neurosurgery.
期刊介绍：
Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments
Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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